Instructions to use Naphula/Slimaki-24B-v1.1-ramplus_tl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Naphula/Slimaki-24B-v1.1-ramplus_tl with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Naphula/Slimaki-24B-v1.1-ramplus_tl")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Naphula/Slimaki-24B-v1.1-ramplus_tl")
model = AutoModelForCausalLM.from_pretrained("Naphula/Slimaki-24B-v1.1-ramplus_tl")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Naphula/Slimaki-24B-v1.1-ramplus_tl with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Naphula/Slimaki-24B-v1.1-ramplus_tl"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Naphula/Slimaki-24B-v1.1-ramplus_tl",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Naphula/Slimaki-24B-v1.1-ramplus_tl

SGLang

How to use Naphula/Slimaki-24B-v1.1-ramplus_tl with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Naphula/Slimaki-24B-v1.1-ramplus_tl" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Naphula/Slimaki-24B-v1.1-ramplus_tl",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Naphula/Slimaki-24B-v1.1-ramplus_tl" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Naphula/Slimaki-24B-v1.1-ramplus_tl",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Naphula/Slimaki-24B-v1.1-ramplus_tl with Docker Model Runner:
```
docker model run hf.co/Naphula/Slimaki-24B-v1.1-ramplus_tl
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

⚠️ Warning: This model can produce narratives and RP that contain violent and graphic erotic content. Adjust your system prompt accordingly, and use Mistral Tekken chat template.

🐌 Ślimaki-24B-v1.1-ramplus_tl

Experimental merge of the new arcee-mergekit method ramplus_tl using the same components as Maginum Cydoms and Slimaki.

It may have some refusals and thus require jailbreaks or ablations in order to uncensor it.

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Methods

This model was merged using the following merge method:

Reinforced Agent Merging Plus (Tensor-Local)

Note: This merge was heavily inspired by Maginum Cydoms

This model was requested as a way to test Arcee's newest mergekit method on the Slimaki/Cydoms components.

Configuration

merge_method: ramplus_tl  
base_model: B:\24B\models--anthracite-core--Mistral-Small-3.2-24B-Instruct-2506-Text-Only
models:  
  - model: B:\24B\models--anthracite-core--Mistral-Small-3.2-24B-Instruct-2506-Text-Only
  - model: B:\24B\models--TheDrummer--Cydonia-24B-v4.3
  - model: B:\24B\models--ReadyArt--4.2.0-Broken-Tutu-24b
  - model: B:\24B\models--zerofata--MS3.2-PaintedFantasy-v2-24B 
  - model: B:\24B\models--TheDrummer--Magidonia-24B-v4.3
  - model: B:\24B\models--TheDrummer--Precog-24B-v1  
  - model: B:\24B\models--zerofata--MS3.2-PaintedFantasy-v3-24B
parameters:  
  epsilon: 0.001  # Increased from 1e-5 to 1e-3 for denser SFT/DPO task vectors  
  r: 0.25         # Increased from 0.1 to 0.2-0.3 for better SFT behavior preservation  
  alpha: 0.4      # Increased from 0.2 to 0.4 for enhanced rescaling
dtype: float32
out_dtype: bfloat16
tokenizer:
  source: base
chat_template: auto
name: 🐌 Ślimaki-24B-v1.1-ramplus_tl

Recommended Settings

In particular temp 1 and topnsigma 1.25 seems to help a lot with improving quality but sometimes you need to reswipe.

Some models are more sensitive to Rep Pen settings, 1.12 is another, you may need to adjust higher for smaller models (like 1.4 if overcooked) or lower for smarter ones.

Not sure about adaptive_p that is a newer setting added to kobold

(bolded kobold non-defaults)